Accurate industrial classification of firms forms the backbone of business surveys, economic policymaking, and international trade analysis. However, national statistics institutes (NSIs) worldwide grapple with the labor intensive manual assignment of International Standard Industrial Classification (ISIC) codes: a process prone to human error, inconsistent across regions, and particularly burdensome for developing economies. This study confronts these challenges by assessing performance of token-overlap (Jaccard), TF-IDF cosine similarity, edit-distance (fuzzy) and SBERT embeddings against human-coded ground truth in classifying firms. Using a dataset of 6588 firms, performance diverges sharply: SBERT attains Accuracy = 0.78 and Weighted F 1 = 0.78 (Cohen’s κ ≈ 0.75 ), while surface methods lag (Fuzzy: Accuracy 0.43; Cosine: 0.31; Jaccard: 0.26). Statistical tests confirms these differences (Cochran’s ( Q = 8320.81 ) with p < 0.001 ) and inter-method agreement is only fair ( κ Fleiss ≈ 0.270 ), motivating a class-level diagnostic approach. Using confusion matrices and Haberman adjusted residuals we expose systematic off-diagonal confusions (notably between manufacturing, professional/service and certain retail/wholesale categories) and identify classes with strong, automatable diagonals versus sparse or ambiguous tails that require human coding.

Paddy data is essential for policymakers to formulate Indonesia's national food security strategies, especially regarding harvested area. Currently, estimates are generated monthly using ground truth data from sampled locations through the Area Sampling Frames (ASF) process. However, the high cost and various field obstacles highlight the need for alternative methods. Satellite Imagery Time Series (SITS) data, mainly Sentinel-1 historical imagery, offers a promising alternative for detecting phenological stages. However, SITS data require machine learning modeling—for example, XGBoost—which has proven successful in several classification tasks. This study presents an alternative approach in West Java province, a major region for paddy production. The workflow includes preliminary analysis, data preprocessing, region-specific modelling, prediction, and estimation. Most regional clusters demonstrate high accuracy in classifying phenological stages, and harvested area patterns closely align with official statistics, demonstrating the effectiveness and potential of this approach.

This article explores recent advancements in the measurement of cross-border financial flows and positions, as introduced in the latest edition of the International Monetary Fund (IMF)'s Integrated Balance of Payments and International Investment Position Manual (BPM7) . It highlights how BPM7 enhances the presentation of foreign direct investment, portfolio investment, and other financial instruments, while emphasizing the policy value of integrating balance of payments (BOP) flows with international investment position (IIP) data through the integrated IIP framework. These innovations offer a more coherent and comprehensive view of global financial linkages, supporting improved analysis of systemic risks and more informed financial stability policymaking.

Poverty in Thailand shows strong spatial dependence that existing administrative boundaries fail to capture, leading to policies that overlook local socioeconomic realities. This study proposes a data-driven regionalization framework to infer geographically coherent “policy regions” that better represent poverty dynamics. Using household-level data from the Thai People Map and Analytics Platform (TPMAP), we analyze spatial autocorrelation across multiple poverty factors through Moran’s statistics and principal component analysis, followed by spatially constrained hierarchical clustering to delineate coherent regions. Bayesian hierarchical and geographically weighted regression models are then employed to examine how education influences household income at provincial, regional, and national levels. Our results identified six regions that reflect more accurately poverty structures than official divisions. Northern and Northeastern Thailand emerge as the regions most affected by low education, income, and savings, while Central Thailand shows higher inequality. The inferred regions demonstrate that spatially contiguous provinces often share similar socioeconomic structures, suggesting that policy targeting should align with these patterns rather than provincial borders. Our findings provide a quantitative foundation for evidence-based regional planning, enabling policymakers to design differentiated yet regionally coordinated interventions. The approach illustrates how spatial statistical modeling can bridge the gap between data analysis and effective poverty-alleviation policy.

This study used data from the 2011 Bangladesh Demographic and Health Survey and the 2011 Bangladesh Population and Housing Census. The Fay-Herriot model was applied to estimate the districts and sub-districts (upazilas) level indicators of undernutrition (stunting, wasting and underweight) for children under five years of age in Bangladesh. At the district level, Narsingdi (63.9%), Feni (19.8%) and Bandarban (62.5%) had the highest prevalence of stunting, wasting and underweight respectively. Among the 64 districts, 28 had stunting prevalence above 40%, wasting prevalence exceeded 15% in 21 districts and 16 districts had underweight prevalence exceeding 40%. Furthermore, all sub-districts within the Sylhet Division were identified as the most severely affected areas for all three indicators of undernutrition. Overall, the Sylhet Division emerged as one of the regions facing the greatest challenges in terms of the three undernutrition indicators for children under five.

Mexico has the highest number of Indigenous Peoples in the American hemisphere: 23.2 million, according to the 2020 census. Yet, Mexico faces significant challenges in the production of official statistics concerning the identification and enumeration of Indigenous populations. This paper presents an analysis of the statistical visibility of Indigenous populations and the methodologies employed in census enumeration in Mexico. The paper, in particular, proposes a different approach to the production of statistics on Indigenous people, one that involves these population groups directly in the survey process, conducts surveys in Indigenous languages, and incorporates questions on self-perception, including identification with the Indigenous group to which they belong. Following the 2020 Census conducted by the National Institute of Statistics and Geography (INEGI) in five Mexican states, an analysis was undertaken using quantitative, descriptive, and comparative methodologies with attention to gender, human rights, and intercultural perspectives. The results demonstrate that Indigenous populations in Mexico are disadvantaged in many aspects. These populations experience greater poverty and food insecurity, lower income, reduced access to health services, education, and water, more precarious housing conditions, and higher rates of child mortality. The most disadvantaged group consists of Indigenous language speakers (ILS), followed by those belonging to or identifying as Indigenous (BIP), compared with the total population. Moreover, substantial disparities among Indigenous language speakers were identified for women and children, and also between States.

In response to a resolution by the United Nations Economic and Social Council (ECOSOC), the Productive Capacities Index (PCI) was developed by UNCTAD as a multidimensional statistical tool to measure, monitor, and benchmark productive capacities through a comprehensive framework, encompassing human capital, natural capital, energy, transport, ICT, institutions, private sector, and structural change. This paper outlines the conceptual foundation of the PCI, the methodological approach used for indicator selection and data transformation, and the statistical processes, particularly Principal Component Analysis, used to derive the composite index. Emphasis is placed on how the PCI supports evidence-based policymaking, facilitates cross-country comparisons, and contributes to statistical development agendas aligned with the 2030 Agenda for Sustainable Development. The PCI serves as a valuable complement to traditional indicators such as GDP, offering insights into the underlying capabilities necessary for economic and social development.

This study investigated awareness of the significance of statistics and statisticians among workers in government and non-government organizations in the Southern Nations, Nationalities, and Peoples’ Region (SNNPR), Ethiopia. Using a multistage random sampling technique, data were collected from 1408 respondents (990 males, 418 females) and analyzed through descriptive statistics and binary logistic regression. Results revealed that 47.6% of male workers and 31.1% of female workers reported awareness of the significance of statistics and statisticians. Logistic regression identified gender, age, marital status, education, work position, organizational practices, and field of specialization as significant predictors. Female, younger, married, and professional workers were less likely to recognize the significance of statistics, whereas workers with MSc degrees and above and those in leadership positions were more likely. Limited access to research resources, lack of awareness training, and affiliation with agriculture, business, or social science fields further reduced awareness. Conversely, awareness was higher among workers who acknowledged statistics as a field of study and those in natural and computational sciences. These findings underscore the importance of targeted awareness training, improved access to statistical resources, and greater integration of statisticians in organizational decision-making.

The ongoing digitalisation is profoundly transforming society, businesses, and the economy at large. Its impact also extends to official statistics, challenging traditional approaches to data collection and analysis. Statistical systems are embracing this shift by advancing along two key lines of work: the adoption of emerging technologies, and the utilisation of new data streams generated by the increasing datafication of our societies. One of the earliest use cases of this approach was the analysis of Online Job Advertisements. From the initial contextual analysis of job portals through web-scraping and data transformation into structured, coded formats, the complex methodology developed is continuously validated to ensure the production of high-quality statistics. The first results disseminated by Eurostat were focused on ICT specialists. According to the indicators used, ICT specialists accounted for 7% of total job advertisements in Europe during the first quarter of 2025, with Luxembourg (18.3%) and Malta (15.4%) leading. A key strength of this data source lies in its granularity, offering insights at the NUTS2 regional level, where statistical information is often limited. As a new statistical source, Online Job Advertisements also face exciting challenges that will shape not only the future of OJA-based information but also the evolution of next-generation statistics.